Papers

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Viewing 1-10 of 736 papers
  • The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning

    Jack Hessel, Jena D. Hwang, Jae Sung Park, Rowan Zellers, Chandra Bhagavatula, Anna Rohrbach, Kate Saenko, Yejin ChoiECCV2022 Humans have remarkable capacity to reason abductively and hypothesize about what lies beyond the literal content of an image. By identifying concrete visual clues scattered throughout a scene, we almost can’t help but draw probable inferences beyond the…
  • Information-Theoretic Measures of Dataset Difficulty

    Kawin Ethayarajh, Yejin Choi, Swabha SwayamdiptaICML2022
    Outstanding Paper Award
    Estimating the difficulty of a dataset typically involves comparing state-of-the-art models to humans; the bigger the performance gap, the harder the dataset is said to be. Not only is this framework informal, but it also provides little understanding of how…
  • Linear Adversarial Concept Erasure

    Shauli Ravfogel, Michael Twiton, Yoav Goldberg, Ryan CotterellICML2022 We formulate the problem of identifying and erasing a linear subspace that corresponds to a given concept, in order to prevent linear predictors from recovering the concept. We model this problem as a constrained, linear minimax game, and show that existing…
  • Dyna-bAbI: unlocking bAbI’s potential with dynamic synthetic benchmarking

    Ronen Tamari, Kyle Richardson, Aviad Sar-Shalom, Noam Kahlon, Nelson H S Liu, Reut Tsarfaty, Dafna Shahaf SEM2022 While neural language models often perform surprisingly well on natural language understanding (NLU) tasks, their strengths and limitations remain poorly understood. Controlled synthetic tasks are thus an increasingly important resource for diagnosing model…
  • Aligning to Social Norms and Values in Interactive Narratives

    Prithviraj Ammanabrolu, Liwei Jiang, Maarten Sap, Hannaneh Hajishirzi, Yejin ChoiNAACL2022 We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games—environments wherein an agent perceives and interacts with a world through natural language. Such interactive agents are…
  • Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback

    Niket Tandon, Aman Madaan, Peter Clark, Yiming YangFindings of EMNLP 2022 Large language models (LMs), while power-ful, are not immune to mistakes, but can be difficult to retrain. Our goal is for an LM to continue to improve after deployment, without retraining, using feedback from the user. Our approach pairs an LM with (i) a…
  • A Dataset for N-ary Relation Extraction of Drug Combinations

    Aryeh Tiktinsky, Vijay Viswanathan, Danna Niezni, Dana Azagury, Yosi Shamay, Hillel Taub-Tabib, Tom Hope, Yoav GoldbergNAACL2022 Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments creates a challenge in identifying effective combination therapies available…
  • Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection

    Maarten Sap, Swabha Swayamdipta, Laura Vianna, Xuhui Zhou, Yejin Choi, Noah A. SmithNAACL2022 Warning : this paper discusses and contains content that is offensive or upsetting. The perceived toxicity of language can vary based on someone’s identity and beliefs, but this variation is often ignored when collecting toxic language datasets, resulting in…
  • Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand

    Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Lavinia Dunagan, Jacob Morrison, Alexander R. Fabbri, Yejin Choi, Noah A. SmithNAACL2022 Natural language processing researchers have identified limitations of evaluation methodology for generation tasks, with new questions raised about the validity of automatic metrics and of crowdworker judgments. Meanwhile, efforts to improve generation models…
  • Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer

    Yanpeng Zhao, Jack Hessel, Youngjae Yu, Ximing Lu, Rowan Zellers, Yejin ChoiNAACL2022 Machines that can represent and describe environmental soundscapes have practical poten-tial, e.g., for audio tagging and captioning. Pre-vailing learning paradigms of audio-text connections have been relying on parallel audio-text data, which is, however…